{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-06-12T07:52:58.034988Z",
     "start_time": "2025-06-12T07:52:58.029821Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import time\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from datetime import datetime\n",
    "from sklearn.metrics import accuracy_score, precision_recall_fscore_support, confusion_matrix, classification_report\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from collections import Counter\n",
    "import jieba"
   ]
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# ------------------------------ 混淆矩阵可视化函数 ------------------------------\n",
    "def plot_confusion_matrix(conf_mat, model_name):\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    sns.heatmap(conf_mat, annot=True, fmt='d', cmap='Blues')\n",
    "    plt.title(f\"{model_name} - Confusion Matrix (THUCNews)\")\n",
    "    plt.xlabel(\"Predicted Label\")\n",
    "    plt.ylabel(\"True Label\")\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# ------------------------------ 数据处理函数 ------------------------------\n",
    "class THUCNewsDataset(Dataset):\n",
    "    def __init__(self, data_dir, split):\n",
    "        self.data_dir = data_dir\n",
    "        self.split = split\n",
    "        self.texts = []\n",
    "        self.labels = []\n",
    "        self.stopwords = {'的', '了', '在', '是', '我', '有', '和', '就', '不', '人', '都', '一', '个', '上', '也', '很', '到', '说', '要', '去', '你', '会', '着', '没有'}\n",
    "\n",
    "        self.load_data()\n",
    "\n",
    "    def load_data(self):\n",
    "        with open(os.path.join(self.data_dir, f\"{self.split}.txt\"), 'r', encoding='utf-8') as f:\n",
    "            for line in f:\n",
    "                line = line.strip()\n",
    "                if not line:\n",
    "                    continue\n",
    "                parts = line.rsplit('\\t', 1)\n",
    "                if len(parts) == 2:\n",
    "                    text = parts[0]\n",
    "                    label = int(parts[1])\n",
    "                    self.texts.append(text)\n",
    "                    self.labels.append(label)\n",
    "\n",
    "    def preprocess(self, text):\n",
    "        words = jieba.cut(text, HMM=True)\n",
    "        filtered = [word for word in words if word not in self.stopwords and len(word) >= 2]\n",
    "        return ' '.join(filtered)\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.texts)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        text = self.texts[idx]\n",
    "        label = self.labels[idx]\n",
    "        processed_text = self.preprocess(text)\n",
    "        return processed_text, label\n",
    "\n",
    "\n",
    "class Vocab:\n",
    "    def __init__(self):\n",
    "        self.word2idx = {}\n",
    "        self.idx2word = []\n",
    "        self.pad_token = '<PAD>'\n",
    "        self.unk_token = '<UNK>'\n",
    "\n",
    "    def build_vocab(self, texts):\n",
    "        all_words = []\n",
    "        for text in texts:\n",
    "            all_words.extend(text.split())\n",
    "        word_counts = Counter(all_words)\n",
    "        for word, _ in word_counts.items():\n",
    "            if word not in self.word2idx:\n",
    "                self.word2idx[word] = len(self.idx2word)\n",
    "                self.idx2word.append(word)\n",
    "\n",
    "        # 添加特殊符号\n",
    "        self.pad_idx = len(self.idx2word)\n",
    "        self.unk_idx = len(self.idx2word) + 1\n",
    "        self.idx2word.extend([self.pad_token, self.unk_token])\n",
    "        self.word2idx[self.pad_token] = self.pad_idx\n",
    "        self.word2idx[self.unk_token] = self.unk_idx\n",
    "\n",
    "    def encode(self, text, max_length=None):\n",
    "        words = text.split()\n",
    "        if max_length is not None:\n",
    "            words = words[:max_length]\n",
    "        indices = [self.word2idx.get(word, self.unk_idx) for word in words]\n",
    "        if max_length is not None and len(indices) < max_length:\n",
    "            indices += [self.pad_idx] * (max_length - len(indices))\n",
    "        return indices"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-06-12T07:52:58.544331Z",
     "start_time": "2025-06-12T07:52:58.528398Z"
    }
   },
   "id": "efd83af9ddc7a3b8",
   "execution_count": 6
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "class RNNClassifier(nn.Module):\n",
    "    def __init__(self, vocab_size, embedding_dim, hidden_dim, output_dim):\n",
    "        super(RNNClassifier, self).__init__()\n",
    "        self.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx=0)\n",
    "        self.rnn = nn.LSTM(embedding_dim, hidden_dim, num_layers=2, bidirectional=True, batch_first=True)\n",
    "        self.fc = nn.Linear(hidden_dim * 2, output_dim)\n",
    "\n",
    "    def forward(self, text):\n",
    "        embedded = self.embedding(text)\n",
    "        output, (hidden, cell) = self.rnn(embedded)\n",
    "        hidden = torch.cat((hidden[-2, :, :], hidden[-1, :, :]), dim=1)\n",
    "        return self.fc(hidden.squeeze(0))\n",
    "\n",
    "\n",
    "# ------------------------------ CNN模型定义 ------------------------------\n",
    "class CNNClassifier(nn.Module):\n",
    "    def __init__(self, vocab_size, embedding_dim, output_dim, n_grams):\n",
    "        super(CNNClassifier, self).__init__()\n",
    "        self.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx=0)\n",
    "        self.convs = nn.ModuleList([\n",
    "            nn.Conv2d(1, 100, (k, embedding_dim)) for k in n_grams\n",
    "        ])\n",
    "        self.fc = nn.Linear(len(n_grams) * 100, output_dim)\n",
    "\n",
    "    def forward(self, text):\n",
    "        embedded = self.embedding(text)\n",
    "        embedded = embedded.unsqueeze(1)\n",
    "        conved = [F.relu(conv(embedded)).squeeze(3) for conv in self.convs]\n",
    "        pooled = [F.max_pool1d(conv, conv.shape[2]).squeeze(2) for conv in conved]\n",
    "        cat = torch.cat(pooled, dim=1)\n",
    "        return self.fc(cat)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-06-12T07:52:59.201133Z",
     "start_time": "2025-06-12T07:52:59.190801Z"
    }
   },
   "id": "63c67c72c5c62f5d",
   "execution_count": 7
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2025-06-12 16:19:37.046358] 文本分类实验开始...\n",
      "Epoch 1\n",
      "RNN - Train Loss: 1.9401, Train Acc: 0.2879\n",
      "Training Time: 92.73 seconds\n",
      "Inference Time: 1.59 seconds\n",
      "Accuracy: 0.2852\n",
      "Precision: 0.3794\n",
      "Recall: 0.2852\n",
      "F1-score: 0.2807\n",
      "Confusion Matrix:\n",
      " [[178  10 189  13   3   1  61 228 142 175]\n",
      " [ 44 114  64  12  10   7  70 399 100 180]\n",
      " [ 96  11 300   9  14   4  28 179 171 188]\n",
      " [ 20  28  60 372   4   8  24 197 135 152]\n",
      " [ 49   4  96  10 401   6  54 161  65 154]\n",
      " [ 43   9 120   5   2  12  79 298 179 253]\n",
      " [ 25  11 121   6   7   9 215 294  51 261]\n",
      " [  3  10  10   6   7   6  94 617  23 224]\n",
      " [ 19   7  68  16  68  14 195 190 319 104]\n",
      " [ 24   3  64   5   4   6  63 393 114 324]]\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.36      0.18      0.24      1000\n",
      "           1       0.55      0.11      0.19      1000\n",
      "           2       0.27      0.30      0.29      1000\n",
      "           3       0.82      0.37      0.51      1000\n",
      "           4       0.77      0.40      0.53      1000\n",
      "           5       0.16      0.01      0.02      1000\n",
      "           6       0.24      0.21      0.23      1000\n",
      "           7       0.21      0.62      0.31      1000\n",
      "           8       0.25      0.32      0.28      1000\n",
      "           9       0.16      0.32      0.21      1000\n",
      "\n",
      "    accuracy                           0.29     10000\n",
      "   macro avg       0.38      0.29      0.28     10000\n",
      "weighted avg       0.38      0.29      0.28     10000\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 800x600 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RNN - Val Loss: 1.9786, Val Acc: 0.2856\n",
      "CNN - Train Loss: 1.9612, Train Acc: 0.2826\n",
      "Training Time: 79.80 seconds\n",
      "Inference Time: 1.45 seconds\n",
      "Accuracy: 0.2546\n",
      "Precision: 0.4430\n",
      "Recall: 0.2546\n",
      "F1-score: 0.2606\n",
      "Confusion Matrix:\n",
      " [[217 135 124  15   1 357   1 144   5   1]\n",
      " [ 46 306 121  18   8 305   0 188   5   3]\n",
      " [114 137 248  10  15 385   3  78   9   1]\n",
      " [ 20 141  68 383   1 301   0  82   4   0]\n",
      " [ 51 140  45  15 404 218   2 110  12   3]\n",
      " [ 38 240  79   5   2 462   1 170   2   1]\n",
      " [ 23 293  30   7   4 390   5 244   2   2]\n",
      " [  8 272  42  10  13 360   1 278  12   4]\n",
      " [ 36 210   4  24  63 238   0 207 214   4]\n",
      " [ 24 292  32   6   8 357   1 244   7  29]]\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.38      0.22      0.28      1000\n",
      "           1       0.14      0.31      0.19      1000\n",
      "           2       0.31      0.25      0.28      1000\n",
      "           3       0.78      0.38      0.51      1000\n",
      "           4       0.78      0.40      0.53      1000\n",
      "           5       0.14      0.46      0.21      1000\n",
      "           6       0.36      0.01      0.01      1000\n",
      "           7       0.16      0.28      0.20      1000\n",
      "           8       0.79      0.21      0.34      1000\n",
      "           9       0.60      0.03      0.06      1000\n",
      "\n",
      "    accuracy                           0.25     10000\n",
      "   macro avg       0.44      0.25      0.26     10000\n",
      "weighted avg       0.44      0.25      0.26     10000\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 800x600 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CNN - Val Loss: 2.0790, Val Acc: 0.2552\n",
      "Epoch 2\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
      "\u001B[31mKeyboardInterrupt\u001B[39m                         Traceback (most recent call last)",
      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[9]\u001B[39m\u001B[32m, line 157\u001B[39m\n\u001B[32m    153\u001B[39m         \u001B[38;5;28mprint\u001B[39m(\u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mCNN - Val Loss: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mval_loss\u001B[38;5;132;01m:\u001B[39;00m\u001B[33m.4f\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m, Val Acc: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mval_acc\u001B[38;5;132;01m:\u001B[39;00m\u001B[33m.4f\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m\"\u001B[39m)\n\u001B[32m    156\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[34m__name__\u001B[39m == \u001B[33m\"\u001B[39m\u001B[33m__main__\u001B[39m\u001B[33m\"\u001B[39m:\n\u001B[32m--> \u001B[39m\u001B[32m157\u001B[39m     main()\n",
      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[9]\u001B[39m\u001B[32m, line 144\u001B[39m, in \u001B[36mmain\u001B[39m\u001B[34m()\u001B[39m\n\u001B[32m    141\u001B[39m \u001B[38;5;28mprint\u001B[39m(\u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mEpoch \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mepoch\u001B[38;5;250m \u001B[39m+\u001B[38;5;250m \u001B[39m\u001B[32m1\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m\"\u001B[39m)\n\u001B[32m    143\u001B[39m \u001B[38;5;66;03m# RNN\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m144\u001B[39m train_loss, train_acc, train_time = train(rnn_model, train_loader, rnn_optimizer, rnn_criterion, device)\n\u001B[32m    145\u001B[39m \u001B[38;5;28mprint\u001B[39m(\u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mRNN - Train Loss: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mtrain_loss\u001B[38;5;132;01m:\u001B[39;00m\u001B[33m.4f\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m, Train Acc: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mtrain_acc\u001B[38;5;132;01m:\u001B[39;00m\u001B[33m.4f\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m\"\u001B[39m)\n\u001B[32m    146\u001B[39m val_loss, val_acc = evaluate(rnn_model, test_loader, rnn_criterion, device, train_time)\n",
      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[9]\u001B[39m\u001B[32m, line 21\u001B[39m, in \u001B[36mtrain\u001B[39m\u001B[34m(model, iterator, optimizer, criterion, device)\u001B[39m\n\u001B[32m     18\u001B[39m     loss.backward()\n\u001B[32m     19\u001B[39m     optimizer.step()\n\u001B[32m---> \u001B[39m\u001B[32m21\u001B[39m     epoch_loss += loss.item()\n\u001B[32m     22\u001B[39m     epoch_acc += acc\n\u001B[32m     24\u001B[39m end_time = time.time()\n",
      "\u001B[31mKeyboardInterrupt\u001B[39m: "
     ]
    }
   ],
   "source": [
    "# ------------------------------ 模型训练函数 ------------------------------\n",
    "def train(model, iterator, optimizer, criterion, device):\n",
    "    model.train()\n",
    "    epoch_loss = 0\n",
    "    epoch_acc = 0\n",
    "    start_time = time.time()\n",
    "\n",
    "    for batch in iterator:\n",
    "        text, label = batch\n",
    "        text = text.to(device)\n",
    "        label = label.to(device)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        predictions = model(text).squeeze(1)\n",
    "        loss = criterion(predictions, label)\n",
    "        acc = accuracy_score(label.cpu().numpy(), torch.argmax(predictions, axis=1).cpu().numpy())\n",
    "\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        epoch_loss += loss.item()\n",
    "        epoch_acc += acc\n",
    "\n",
    "    end_time = time.time()\n",
    "    train_time = end_time - start_time\n",
    "\n",
    "    return epoch_loss / len(iterator), epoch_acc / len(iterator), train_time\n",
    "\n",
    "\n",
    "def evaluate(model, iterator, criterion, device, train_time):\n",
    "    model.eval()\n",
    "    epoch_loss = 0\n",
    "    epoch_acc = 0\n",
    "    all_labels = []\n",
    "    all_preds = []\n",
    "    start_time = time.time()\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for batch in iterator:\n",
    "            text, label = batch\n",
    "            text = text.to(device)\n",
    "            label = label.to(device)\n",
    "\n",
    "            predictions = model(text).squeeze(1)\n",
    "            loss = criterion(predictions, label)\n",
    "            acc = accuracy_score(label.cpu().numpy(), torch.argmax(predictions, axis=1).cpu().numpy())\n",
    "\n",
    "            epoch_loss += loss.item()\n",
    "            epoch_acc += acc\n",
    "\n",
    "            all_labels.extend(label.cpu().numpy())\n",
    "            all_preds.extend(torch.argmax(predictions, axis=1).cpu().numpy())\n",
    "\n",
    "    inference_time = time.time() - start_time\n",
    "\n",
    "    # 计算评估指标\n",
    "    accuracy = accuracy_score(all_labels, all_preds)\n",
    "    precision, recall, f1, _ = precision_recall_fscore_support(all_labels, all_preds, average='weighted')\n",
    "    conf_matrix = confusion_matrix(all_labels, all_preds)\n",
    "    class_report = classification_report(all_labels, all_preds)\n",
    "\n",
    "    # 输出结果\n",
    "    print(f\"Training Time: {train_time:.2f} seconds\")\n",
    "    print(f\"Inference Time: {inference_time:.2f} seconds\")\n",
    "    print(f\"Accuracy: {accuracy:.4f}\")\n",
    "    print(f\"Precision: {precision:.4f}\")\n",
    "    print(f\"Recall: {recall:.4f}\")\n",
    "    print(f\"F1-score: {f1:.4f}\")\n",
    "    print(\"Confusion Matrix:\\n\", conf_matrix)\n",
    "    print(\"Classification Report:\\n\", class_report)\n",
    "\n",
    "    # 绘制混淆矩阵热力图\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues')\n",
    "    plt.title(f\"{type(model).__name__} - Confusion Matrix (THUCNews)\")\n",
    "    plt.xlabel(\"Predicted Label\")\n",
    "    plt.ylabel(\"True Label\")\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f\"{type(model).__name__}_confusion_matrix.png\")\n",
    "    plt.show()\n",
    "\n",
    "    return epoch_loss / len(iterator), epoch_acc / len(iterator)\n",
    "\n",
    "\n",
    "# ------------------------------ 主函数 ------------------------------\n",
    "def main():\n",
    "    print(f\"[{datetime.now()}] 文本分类实验开始...\")\n",
    "\n",
    "    DATA_DIR = r\"D:\\Machine_learning\\jiqixuexi\\THUCNews-txt\"\n",
    "    CLASS_PATH = os.path.join(DATA_DIR, \"class.txt\")\n",
    "\n",
    "    # 加载数据\n",
    "    train_dataset = THUCNewsDataset(DATA_DIR, 'train')\n",
    "    test_dataset = THUCNewsDataset(DATA_DIR, 'test')\n",
    "\n",
    "    # 构建词汇表\n",
    "    all_texts = [train_dataset.texts, test_dataset.texts]\n",
    "    vocab = Vocab()\n",
    "    vocab.build_vocab(all_texts[0] + all_texts[1])\n",
    "\n",
    "    # 定义超参数\n",
    "    EMBEDDING_DIM = 100\n",
    "    HIDDEN_DIM = 256\n",
    "    OUTPUT_DIM = len(set(train_dataset.labels))\n",
    "    N_GRAMS = [3, 4, 5]\n",
    "    BATCH_SIZE = 64\n",
    "    NUM_EPOCHS = 5\n",
    "\n",
    "    # 定义数据加载器\n",
    "    def collate_batch(batch):\n",
    "        texts, labels = zip(*batch)\n",
    "        texts = [vocab.encode(text) for text in texts]\n",
    "        max_length = max(len(text) for text in texts)\n",
    "        padded_texts = [text + [vocab.pad_idx] * (max_length - len(text)) for text in texts]\n",
    "        return torch.tensor(padded_texts), torch.tensor(labels)\n",
    "\n",
    "    train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, collate_fn=collate_batch)\n",
    "    test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, collate_fn=collate_batch)\n",
    "\n",
    "    # 加载类别标签\n",
    "    label_map = {}\n",
    "    with open(CLASS_PATH, 'r', encoding='utf-8') as f:\n",
    "        for idx, line in enumerate(f):\n",
    "            label_map[idx] = line.strip()\n",
    "\n",
    "    # 初始化模型、优化器和损失函数\n",
    "    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "    # RNN模型\n",
    "    rnn_model = RNNClassifier(len(vocab.word2idx), EMBEDDING_DIM, HIDDEN_DIM, OUTPUT_DIM).to(device)\n",
    "    rnn_optimizer = optim.Adam(rnn_model.parameters())\n",
    "    rnn_criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "    # CNN模型\n",
    "    cnn_model = CNNClassifier(len(vocab.word2idx), EMBEDDING_DIM, OUTPUT_DIM, N_GRAMS).to(device)\n",
    "    cnn_optimizer = optim.Adam(cnn_model.parameters())\n",
    "    cnn_criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "    # 训练模型\n",
    "    for epoch in range(NUM_EPOCHS):\n",
    "        print(f\"Epoch {epoch + 1}\")\n",
    "\n",
    "        # RNN\n",
    "        train_loss, train_acc, train_time = train(rnn_model, train_loader, rnn_optimizer, rnn_criterion, device)\n",
    "        print(f\"RNN - Train Loss: {train_loss:.4f}, Train Acc: {train_acc:.4f}\")\n",
    "        val_loss, val_acc = evaluate(rnn_model, test_loader, rnn_criterion, device, train_time)\n",
    "        print(f\"RNN - Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.4f}\")\n",
    "\n",
    "        # CNN\n",
    "        train_loss, train_acc, train_time = train(cnn_model, train_loader, cnn_optimizer, cnn_criterion, device)\n",
    "        print(f\"CNN - Train Loss: {train_loss:.4f}, Train Acc: {train_acc:.4f}\")\n",
    "        val_loss, val_acc = evaluate(cnn_model, test_loader, cnn_criterion, device, train_time)\n",
    "        print(f\"CNN - Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.4f}\")\n",
    "\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ],
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